Ce cours présente les outils et les bonnes pratiques MLOps pour déployer, évaluer, surveiller et exploiter des systèmes de production de ML sur Google Cloud. Le MLOps est une discipline axée sur le déploiement, le test, la surveillance et l'automatisation des systèmes de ML en production. Les ingénieurs en machine learning utilisent des outils pour améliorer et évaluer en permanence les modèles déployés. Ils collaborent avec des data scientists (ou peuvent occuper ce poste) qui développent des modèles permettant de déployer de manière rapide et rigoureuse les solutions de machine learning les plus performantes.
MLOps (Machine Learning Operations) Fundamentals en Français
MLOps (Machine Learning Operations) Fundamentals en FrançaisGoogle Cloud
About this Course
What you will learn
Identify and use core technologies required to support effective MLOps.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Adopt the best CI/CD practices in the context of ML systems.
Implement reliable and repeatable training and inference workflows.
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Présentation du cours "MLOps Fundamentals"
Ce module fournit une présentation du cours
Quand et pourquoi utiliser le MLOps ?
Dans ce module, nous allons nous intéresser au machine learning d'un point de vue opérationnel. Pour cela, nous allons examiner le système dans son ensemble, de la définition du problème jusqu'à la solution.
Connaître les principaux composants de Kubernetes (facultatif)
Ce module est facultatif.
Présentation d'AI Platform Pipelines
Dans ce module, nous allons aborder un produit Google Cloud, AI Platform Pipelines, qui rend le MLOps facile, fluide et évolutif grâce aux services Google Cloud.
Entraînement, réglage et inférence sur AI Platform
Dans ce module, nous allons découvrir comment procéder manuellement à l'entraînement, au réglage et à l'inférence d'un modèle à partir du notebook Jupyter sur AI Platform.
Kubeflow Pipelines sur AI Platform
Dans ce module, nous allons automatiser le processus d'entraînement et de réglage que nous avons étudié précédemment à l'aide d'un pipeline Kubeflow. Plutôt que d'avoir à déclencher manuellement toutes les étapes de ce processus dans le notebook JupyterLab, nous pouvons déclencher l'ensemble du processus en un clic après en avoir exprimé les différentes étapes dans un pipeline Kubeflow.
CI/CD pour Kubeflow Pipelines sur AI Platform
Dans ce module, nous allons nous intéresser à l'intégration et au déploiement continus (CI/CD) pour les pipelines Kubeflow. Nous savons comment créer un pipeline Kubeflow automatisé. Mais comment pouvons-nous faire en sorte qu'il s'inscrive dans une pile d'intégration continue ? L'objectif est de recompiler les éléments du pipeline immédiatement lorsque du nouveau code d'entraînement est transféré vers le dépôt correspondant.
Ce module récapitule les points abordés dans le cours.
Ressources du cours
Liens vers les PDF de tous les modules
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